There are numerous challenges that must be addressed to provide accurate detection and classification of possible targets based on imagery data. One such challenge is distinguishing targets from the surrounding/background environment. For example, in infrared imagery, the intensities of target signatures may differ very little from that of the background, especially at times of the day when the background and target temperatures are similar. On the other hand, detection algorithms that discriminate targets from background on the basis of target intensity can produce false detections due to the presence of particularly hot or cold objects, including fires or heated objects. To increase the probability of correctly detecting desired targets and rejecting decoys and incidental false alarms, additional target feature data are desirable. Features that can lead to improved probabilities of detection or correct classification includes polarization characteristics for the image of the target.
Polarimetric imaging is a production of multiple images, wherein each image is a response to a particular polarization of light. One of the advantages of using the polarimetric imaging in detecting and classifying targets in an image is target specificity. In particular, man-made objects typically emit and/or reflect linearly polarized light to a greater degree than natural background clutter. In addition, a polarization angle can be used to infer an angular aspect of target surfaces when the target is resolved into multiple pixels in the image. Although polarizations can generally be described as linear, circular, or elliptical, the most useful polarizations for target detection and classification are linear polarizations at different angles or metrics that can be directly related to linear metrics. Stokes Parameters are often used to describe the polarization characteristics of light. The Stokes Parameters related to linear measurements are often described by the following relationship:
                                          S            0                    =                                                    I                H                            +                                                I                  V                                ⁢                                                                  ⁢                or                ⁢                                                                  ⁢                                  S                  0                                                      =                                          (                                                      I                    H                                    +                                      I                    V                                    +                                      I                                          +                      45                                                        -                                      I                                          -                      45                                                                      )                            /              2                                      ⁢                                  ⁢                              S            1                    =                                    I              H                        -                          I              V                                      ⁢                                  ⁢                              S            2                    =                                    I                              +                45                                      -                          I                              -                45                                                    ⁢                                  ⁢                  DOLP          =                                                                                                                S                      1                      2                                        +                                          S                      2                      2                                                                                        S                  0                                            ⁢                                                          ⁢              and              ⁢                                                          ⁢              AOLP                        =                                          1                2                            ⁢                                                tan                                      -                    1                                                  ⁡                                  (                                                            S                      2                                                              S                      1                                                        )                                                                                        (        1        )            where IH, IV, I+45, and I−45 are linear measurements at 0, 90, 45, and −45 degrees, respectively, and the Stokes Parameters S0, S1, and S2 are measures of unpolarized, horizontal/vertical, and diagonal polarizations respectively. DOLP is a degree of linear polarization, and AOLP is the angle of linear polarization.
Construction of high-quality polarization imagery data is not without challenges. The process of separating and processing the responses to the polarized components of light can produce images that suffer from poor signal-to-noise ratio (SNR), poor polarization isolation, and inaccurate registration of the multiple polarization images with respect to one another.
There are numerous ways of implementing polarimeters to obtain polarimetric imagery data including time division, amplitude division, aperture division, and focal-plane-division. Each implementation has advantages and disadvantages.
For example, the time division implementation typically requires sequential collection of multiple linear polarization images by rotating a linear polarizing filter in front of the imaging device. The SNR for any polarization component is typically low compared with the total energy of all polarizations. The time division implementation has the advantage of being relatively simple and the registration among polarizations may not be an issue, since the image detector array itself requires no mechanical movement. However, time delays between each polarization may cause polarization registration issues if either the target or the detector is moving.
The amplitude division method divides a single light path into four paths by means of beam splitters. Each divided path is processed through polarization filters before reaching its respective detector array. In this case the SNR is reduced due to both the polarization filters and the beam splitters. In addition, registration of the individual polarization components may be challenging, especially over temperature variations and mechanical vibration. This type of system is also expensive and bulky; however, the amplitude division method does provide good resolution and minimal motion artifacts, since all polarizations are derived simultaneously.
The aperture division method sub-divides a physical aperture such that each sub-aperture is filtered for a single polarization. Similar to previous methods above, the SNR is reduced due to polarization filtering and also due to the reduced energy collected by the smaller sub-aperture. In addition, the resolution of the image is typically reduced by a factor of at 2 in both the x-direction and the y-direction due to the reduced size of each of the sub-apertures assigned to each polarization sensor.
The focal-plane-array, (FPA) implementation uses four micro-polarizers in a 2×2 array resolvable into 4 sub-pixels as shown in FIG. 1, each with a different polarization filter. Similar to previous methods above, the SNR is reduced due to the polarization filtering. In order for all four of the sub-pixels to respond to a commonly resolvable image, the system is designed so that the point spread function (PSF) encompasses four sub-pixels. This dilutes the received energy per polarization by another factor of four compared with a PSF that is matched to a single pixel. In addition, the resolution of the image is reduced by a factor of 2 in both the x-direction and the y-direction due to the larger PSF. The four polarized images derived from the FPA are naturally mis-registered by half of the resolvable pixel width (or by one sub-pixel interval) in x and/or y, but this relative registration is fixed and known. In this system, target or sensor motion does not cause additional registration problems.
In many applications, the polarimetric imagery acquisition methods described above yield inadequate SNR. All of the methods described above sacrifice resolution and/or registration stability or accuracy. The micro-polarizer FPA implementation appears to yield a favorable combination of stable relative registration, simplicity, low-volume, and low cost. Since the FPA uses individually dedicated image sensors (sub-pixels) with a fixed geometric arrangement between polarization elements, the registration of the polarization images is stable and known. In addition, motion artifacts caused by a moving target or sensor are minimized since there is no time delay between each polarization image. However, the conventional system design using the micro-polarizer FPA suffers from less than optimum resolution and an additional SNR loss, since the PSF is sized to illuminate four pixels in order to obtain all polarization measurements. This results in degradation of resolution by factor of 2 in each spatial direction and reduction of SNR by factor of 4 in addition to the necessary loss of energy due to the polarizing filters.